Wang · JCI insight 2017 · computational network analysis and cohort genetic association study · n=?

Network analysis of the genomic basis of the placebo effect.

Cited 46 times in the scientific literature.

Level 5 - mechanism / opinion, no new human data

Level 5 by design analogy (computational interactome network analysis with secondary cohort genetic validation).

PubMed 28570268 · doi:10.1172/jci.insight.93911 · record verified 2026-08-29

What was done

The authors conducted a network analysis of the comprehensive human interactome using a seed-connector algorithm to identify a "placebome" module. They tested this module for enrichment in biological pathways and brain-specific proteins. The module was validated using genetic data from the placebo arm of the Women's Genome Health Study (WGHS) cohort by assessing enrichment for outcome-modifying SNPs. Finally, they calculated network proximity between the placebome module, specific disease modules, and drug target modules to evaluate relationships with clinical placebo response strength and drug interactions.

What was found

The placebome module was significantly enriched with neurotransmitter signaling pathways and brain-specific proteins. Validation in the WGHS trial showed significant enrichment for genes with SNPs modifying placebo-arm outcomes. The network proximity between the placebome module and disease modules significantly correlated with the strength of the placebo effect in corresponding diseases, and proximity to drug target modules identified pathways indicating placebo-drug interactions. The abstract does not report specific numerical values, effect sizes, or p-values.

Why it matters

This framework characterizes a biological network underlying the placebo effect, demonstrating how genetic variation may influence placebo responsiveness. The findings offer a computational approach to anticipate placebo response strength across different diseases and account for placebo-drug interactions in clinical trials.

Limits

The abstract provides no exact sample sizes, effect sizes, or statistical figures. The analysis relies on computational interactome networks and bioinformatic predictions, which are subject to curation biases. Genetic validation was limited to a single trial cohort of female participants (WGHS), which may not generalize across broader clinical populations or conditions.

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